Researchers urge treating AI agents as untrusted systems, warning of crypto security risks

The definitive story of how Claude Code and OpenClaw kicked off computing’s biggest transformation possibly ever.

ClickUp CEO Zeb Evans announced last week that the collaboration software company, last valued at $4 billion in 2021, had laid off twenty-two percent of its workforce — framing the cuts not as cost reduction but as a structural shift toward AI-driven operations. The company has deployed roughly 3,000 internal AI agents to handle complex tasks, with […]

The gap between what agentic AI promises and what it actually delivers in live environments is now one of the most consequential engineering problems in the industry. It is also, frustratingly, one that the field has been slow to name precisely, let alone fix...

AI agents look brilliant in a demo because demos are friendly worlds. The data is curated, the tools behave, and nothing important changes while the agent is in mid-thought. Production is the opposite: data arrives late, facts conflict, permissions bite, APIs time out, and the underlying state changes constantly. That gap is why early “agents in production” often get scoped down to something safer: read-only assistants, human-in-the-loop workflows, or narrow domains with heavily curated data. Several high-profile deployments have also been scaled back after meeting messy real-world constraints. Rather than being a verdict on autonomy, these stumbles are a reminder that autonomy is unforgiving. Small cracks in your data stack become large cracks in agent behavior. The same pattern shows up whenever agents move from toy workflows to systems with real state. As scope increases, weak guarantees create predictable symptoms: overconfident actions on stale data, brittle reasoning when meaning

Amid the global crackdown on online gambling and prediction markets, Indonesia has joined the list of jurisdictions imposing restrictions on Polymarket and similar platforms after a bet on the President’s term drew online attention. Related Reading: Crypto Payments Go Autonomous As AI Agents Execute 176M Transactions Indonesia Blocks Access To Polymarket Indonesia recently blocked access […]

AI agents are becoming increasingly popular among crypto users, with Circle CEO Jeremy Allaire predicting that billions of AI agents will be operating within five years.

The agent economy is reshaping financial markets. Open-source agent frameworks are accelerating autonomous financial activity, with AI agents increasingly executing trades, managing portfolios, and interacting directly with exchanges. Yet the financial  infrastructure supporting this shift has not evolved at the same pace. CoinQuant, the AI-powered no-code trading platform that has

Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production. To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows. Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion. For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser